Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
This addresses the challenge of distributed cluster enumeration in sensor networks, offering an adaptive solution for dynamic environments, though it is incremental as it builds on existing feature-based methods by adding adaptability.
The paper tackles the problem of adaptively estimating the time-varying number of clusters in distributed networks, such as wireless sensor networks, without prior knowledge, and demonstrates that Gravitational Clustering achieves robust performance with numerical experiments showing convergence and applicability to real-world scenarios like multi-view camera networks.
Distributed signal processing for wireless sensor networks enables that different devices cooperate to solve different signal processing tasks. A crucial first step is to answer the question: who observes what? Recently, several distributed algorithms have been proposed, which frame the signal/object labelling problem in terms of cluster analysis after extracting source-specific features, however, the number of clusters is assumed to be known. We propose a new method called Gravitational Clustering (GC) to adaptively estimate the time-varying number of clusters based on a set of feature vectors. The key idea is to exploit the physical principle of gravitational force between mass units: streaming-in feature vectors are considered as mass units of fixed position in the feature space, around which mobile mass units are injected at each time instant. The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i.e., gravitational clusters. By sharing estimates among neighboring nodes via a diffusion-adaptation scheme, cooperative and distributed cluster enumeration is achieved. Numerical experiments concerning robustness against outliers, convergence and computational complexity are conducted. The application in a distributed cooperative multi-view camera network illustrates the applicability to real-world problems.